"It appears only two previous papers [1,2] consider the use of optimization methods in the reconstruction of vehicular crashes involving the collision. Both deal with the Optimizer utility that is part of the PC-CRASH reconstruction software[3]"

This paper describes an automatic iterative procedure which can quickly and efficiently iterate to a "best match" of the physical evidence with SMAC. Quantitative measures of the overall "fit" to the evidence, which guide the procedure, are discussed. Representative results from applications to experimental tests are presented

from the PROBLEM STATEMENT

"Many different optimization and error minimization routines were investigated [12-16]. A fundamental problem with the use of many of the investigated control algorithms was the inherent requirement that the functions must be continuous and/or linear. The collision and trajectories of vehicles can be highly non-linear events. Minor variations in starting conditions (i.e., speed, impact location) can produce major changes in the resulting rest positions (X, Y, PSI) and discontinuities in the calculated error evaluation terms. For example, during decelerations of the linear and angular velocities, as a vehicle rotates while it travels from separation to rest, the vehicle may “shoot off” tangentially in what has been described as a “dog leg” type of trajectory at any time that the velocity vector aligns with the longitudinal axis. Traditional function minimization techniques which require the evaluation of some form of derivatives (e.g., Cramer's rule, Newton’s method) or include the assumption of a linear function (Powell’s method, Broyden’s method) were found to fail in many instances where step changes were produced in the "function" by minor alterations of the variables. The final form of the function minimization routine is a customized routine roughly based upon an adaptation of the downhill simplex method of Nelder and Mead [17] and Press [15]"

In 1975, Jones [9] presented a plan for automatic iteration of SMAC to make the SMAC program "user orientated" so that users can operate the program with "ease". The program modifications iterated only the vehicle speeds. The initial starting speeds of the vehicles for the SMAC runs were based on the results of the START2 routine [8, p88-96]. The SMAC predicted positions of rest were then compared with the measured positions of rest. A conclusion of the research was that "the results suggest that to obtain unique solutions for certain accident configurations it is insufficient to optimize on rest positions alone".
In 1980, Moffatt and Byrd [10] created a program entitled AUTOSMAC which automatically adjusted the starting conditions of the simulation in an attempt to match the target conditions. The target conditions which "may be used" included:

The variables which were optimized included the vehicle speeds, steering and braking. They found "the most difficult variables are steering and braking because collision outcomes such as rest positions relate in nonlinear ways to steering and braking". An optimization method was developed in view of the fact that "standard optimization procedures which make many trials of the system would be impractical because the SMAC program is costly to run". The authors found that only a few of the desired target conditions could be optimized due to limitations on time and budget.
These early research projects on the automatic iteration of SMAC were severely constrained by computer time and resources. The research was performed on mainframe computers where the cost of applications included charges for CPU time and memory resources. SMAC computer runs on mainframe computers were expensive, with "two or three iterations at a cost of $40-$60" [9]. From the early 1980's until the mid 90's, phenomenal advances in computer technology, particularly microprocessor technology [11], moved the mainstay of scientific computing from mainframes to mini-computers to personal computers. With personal computers there are no charges for CPU or memory usage.
In 1997, as part of research contained in the paper CRASH-97 [3], the authors implemented an update to the trajectory simulation routine of CRASH which included the automatic iteration of SMAC to simulate the trajectory of vehicles between separation and rest. The SMAC program was thereby used to refine and test the results of the CRASH program. The automatic iteration included adaptations of optimization techniques for error reduction and convergence in iterative solutions.
In 2001, with the advent of the Gigahertz+ Pentium 4 machine, the authors concluded that it was feasible and practical to extend the automatic iteration of SMAC development started with the CRASH-97 research and to create an automatic iteration scheme for the complete SMAC program, including approach, collision and separation-to-rest phases.

Make that 5 papers missed!
In doing a literature search for our publication related to the automatic iteration of msmac3D we came across the following 2009 ASME paper on optimization methodology on vehicular crash reconstruction:

In vehicular crash reconstruction, software packages such as PC-Crash, SMAC (Simulation Model of Automobile Collisions), WinSmash and HVE (Human Vehicle Environment) use physical evidences such as tire marks along with measurements of the deformed vehicles and photographs of the accident scene to determine the crash energy, impact velocity, and Principal Direction Of Force (PDOF). However, accurate determination of these parameters requires more sophisticated numerical methods, such as Finite Element (FE) modeling. At present, multiple runs of FE models need to be performed on a trial-and-error basis before the model predicted results are consistent with the actual ones. An optimization method to quickly and accurately determine key sensitive parameters in vehicular accident reconstruction is desired. We propose the use of Kriging model and sequential quadratic programming in conjunction with Latin Hypercube Sampling (LHS) to minimize the time needed for reconstruction and minimize the disparity between the actual and FE model predicted vehicular deformations. A selected number of modeling parameters, namely the velocity of impact, PDOF and initial impact position, are varied using this optimization approach until the deformation of six points measured on the impacted vehicle closely matches those measured in real world case. The optimization is performed in two stages. In the first stage, an approximated model was created by simplifying detailed FE models of the vehicles involved to reduce the simulation time without sacrificing accuracy. In the second stage, an assessment index ‘E’, the objective function, is maximized. To improve computational efficiency, the Kriging model is employed. The sampling points are distributed uniformly over the entire design space using the LHS. For evaluating the approximated model’s performance, the regression parameter is used as the error indicator. The objective functions based on approximated models are optimized using a sequential quadratic programming which has a higher efficiency and better convergence. Results show that through the application of this method, the deformations of the key points are in accord to the measured deformation within a small window of variability. The average difference between the deformation measured from the actual crash and that calculated from FE simulation using the optimum parameters as inputs is around 31 mm. The difference in the assessment index calculated from FE simulation with optimal assessment parameters and that from the Kriging model is only 1%. The proposed optimization methodology is a good tool to promptly reveal key parameters in a crash while simultaneously providing scientific basis for crash reconstruction.

ABSTRACT:One of the most interesting and challenging applications in engineering is the solution of inverse problems. In this kind of problem we have as an example the scientific approach of the ground vehicle accident reconstruction. Another directly related theme is collisions analysis, either in the context of an accident reconstruction, or in the context of the vehicle crashworthiness, associated to its structural integrity and their occupants’ passive safety. In this paper the application of optimization techniques for the treatment of ground vehicles collision inverse problem using models of rigid or flexible bodies is discussed. We present an example of the classic approach, the conjugated directions method, and one of the modern, the genetic algorithms.

ABSTRACT: PC-Crash simulations of staged collisions require dozens of parameters describing vehicle and impact parameters. The Collision Optimizer will vary initial speeds and impact parameters to obtain a best fit to a desired end state, but vehicle parameters are left unchanged. The present paper allows these other parameters to vary in thousands of combinations, re-optimizing the solution in each to find the relationships between the previously fixed parameters and the resulting impact speeds. The results show that tire friction and vehicle inertial properties have the most influence on impact speeds. Other parameters have little influence on the results.